自动测量语音可理解性模型
Johannes Tröger1, Andreas Rouvalis1, Felix Dörr1
1ki:elements GmbH, Saarbrücken, Germany.
Amyotrophic lateral sclerosis & frontotemporal degeneration
|August 25, 2025
概括
自动语音分析准确地测量了肌缩侧面硬化症 (ALS) 的语音理解能力. 这种数字工具有助于监测疾病的进展和腹筋功能,从而有可能为患有ALS的患者提供更早的干预.
科学领域:
- 神经学
- 语音病理学
- 生物医学工程
背景情况:
- 肌缩侧面硬化 (ALS) 导致运动神经元的逐渐退化,导致明显的语言障碍和沟通效率下降.
- 对语音可理解性的客观和可扩展的评估方法对于跟踪ALS疾病进展和治疗反应至关重要.
- 目前的临床评估可能并不总能捕捉到语言理解能力的微妙,早期变化.
研究的目的:
- 调查自动化数字语音可理解度的有用性,以评估和监测患有ALS (plwALS) 的患者的语音和气泡功能.
- 使用自然图片描述,比较plwALS和健康对照组 (HC) 的语音可理解性.
- 评估自动语音可理解性得分与功能和疾病进展的临床测量之间的相关性.
主要方法:
- 使用自动语音分析管道分析了44个plwALS和49个HC的语音记录,以获得可理解性得分.
- 这项研究采用了两种评估点的横截面和纵向设计.
- 可理解性得分与圆柱体特异性临床测量和总体ALS得分相关.
主要成果:
- 与HC相比,plwALS的语音可理解性显著降低.
- 与脊柱发病ALS患者相比,患有气泡发病ALS患者的理解能力较低.
- 自动语音分析检测出纵向可理解性恶化,并且在某些情况下比标准临床评分更敏感.
结论:
- 自动语音可理解性评估是一个有价值的客观标志物,用于监测ALS的进展和气泡功能.
- 这种数字工具可以补充标准的临床评估,可能有助于早期治疗ALS.
- 这些发现支持使用自动语音分析来改善ALS的临床治疗.
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